Stress in university students and cardiovascular response to academic stressors

Introduction: University students are frequently exposed to events that can cause stress and anxiety, producing elevated cardiovascular responses. Repeated exposure to academic stress has implications to students’ success and well-being and may contribute to the development of long-term health problems. Objective: To identify stress levels and coping strategies in university students and assess the impact of stress experience in heart rate variability (HRV). Methods: 17 university students, 19-23 years, completed the University Students Stress Inventory, the Depression Anxiety Stress Scales and the Ways of Coping Questionnaire. Two 24h-Holter recordings were performed, on academic activity days, including one of them an exam situation. Results: Students tend to present moderate stress levels, and prefer problem-focused coping strategies in order to manage stress. Exam situations are perceived as significant stressors. Although we found no significant differences in HRV (SDNN), between days with and without an exam, we registered a lower SDNN score and a variation in heart rate (HR) related to exam situation (maximum HR peak at 10 minutes before the exam, and total HR recovery 20 minutes after the exam), reflecting sympathetic activation due to stress. Conclusions: These results suggest that academic events, especially those related to exam situations, are the cause of stress in university students, with implications at cardiovascular level, underlying the importance of interventions that help these students improve their coping skills and optimize stress management, in order to improve academic achievement and promote well-being and quality of life.

"Gestão Oficinal no Setor Automóvel” - Caso de estudo na AMC, Lda.

A indústria automóvel no que respeita à gestão oficinal de qualidade é uma referência de dimensão e nível mundial. O constante avanço da indústria automóvel, o ciclo de vida dos produtos (automóveis) cada vez mais curtos e a competitividade entre empresas, requer uma procura constante de novas ferramentas de melhoria na gestão oficinal. Neste trabalho, centrado na gestão oficinal no setor automóvel, procuram-se introduzir novas abordagens que visem combater o decréscimo da rentabilidade neste setor. Como se depreende do exposto anteriormente, o objetivo principal desta dissertação é propor e avaliar (validar) propostas de melhoria que visem o aumento da rentabilidade financeira da empresa AMC – Auto Mecânica de Cambra, Lda. Com vista à prossecução deste objetivo foram definidos os seguintes objetivos intercalares na AMC: - Análise e levantamento dos problemas; - Identificação de potenciais causas; - Formulação de ações de propostas de melhoria na gestão da empresa; - Programação das ações e avaliação de resultados alcançados; A estrutura definida nesta dissertação após a sua introdução no capítulo 1 centra-se no capítulo 2 na apresentação da empresa AMC, com a descrição dos problemas levantados atendendo às vertentes mais críticas. Seguidamente, no capítulo 3, com ferramentas de análise de problemas, serão encontradas as causas raiz destes problemas. Posteriormente no capítulo seguinte serão criados planos de ações para a resolução das causas. No capítulo 4 as ações serão implementadas e avaliadas para garantir a sua eficácia e eficiência. Por fim, no capítulo 5 são descritas as conclusões e os desenvolvimentos futuros enquadráveis com a melhoria contínua da AMC. Como conclusão, este trabalho, mostra que várias propostas de melhoria implementadas trouxeram ganhos efetivos para a empresa e que futuras propostas são potencialmente válidas num futuro próximo. Esta dissertação com ligação direta à AMC tem igualmente dados que podem ser extrapolados e adaptados a outras empresas do mesmo setor. O objetivo principal deste estudo é a análise e o levantamento dos problemas com a utilização de ferramentas tais como: Benchmarking, Diagrama causa-efeito, ciclo PDCA, análise de SWOT e indicadores operativos da oficina.

Fast mobility support in low-power wireless networks: smart-HOP over RPL/6LoWPAN

With the emergence of low-power wireless hardware new ways of communication were needed. In order to standardize the communication between these low powered devices the Internet Engineering Task Force (IETF) released the 6LoWPAN stand- ard that acts as an additional layer for making the IPv6 link layer suitable for the lower-power and lossy networks. In the same way, IPv6 Routing Protocol for Low- Power and Lossy Networks (RPL) has been proposed by the IETF Routing Over Low power and Lossy networks (ROLL) Working Group as a standard routing protocol for IPv6 routing in low-power wireless sensor networks. The research performed in this thesis uses these technologies to implement a mobility process. Mobility management is a fundamental yet challenging area in low-power wireless networks. There are applications that require mobile nodes to exchange data with a xed infrastructure with quality-of-service guarantees. A prime example of these applications is the monitoring of patients in real-time. In these scenarios, broadcast- ing data to all access points (APs) within range may not be a valid option due to the energy consumption, data storage and complexity requirements. An alternative and e cient option is to allow mobile nodes to perform hand-o s. Hand-o mechanisms have been well studied in cellular and ad-hoc networks. However, low-power wireless networks pose a new set of challenges. On one hand, simpler radios and constrained resources ask for simpler hand-o schemes. On the other hand, the shorter coverage and higher variability of low-power links require a careful tuning of the hand-o parameters. In this work, we tackle the problem of integrating smart-HOP within a standard protocol, speci cally RPL. The simulation results in Cooja indicate that the pro- posed scheme minimizes the hand-o delay and the total network overhead. The standard RPL protocol is simply unable to provide a reliable mobility support sim- ilar to other COTS technologies. Instead, they support joining and leaving of nodes, with very low responsiveness in the existence of physical mobility.

Consumo inteligente de energia elétrica

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Redes de Comunicação e Multimédia

Design of New Materials for Passive Vibration Control

The objective of this contribution is to extend the models of cellular/composite material design to nonlinear material behaviour and apply them for design of materials for passive vibration control. As a first step a computational tool allowing determination of optimised one-dimensional isolator behaviour was developed. This model can serve as a representation for idealised macroscopic behaviour. Optimal isolator behaviour to a given set of loads is obtained by generic probabilistic metaalgorithm, simulated annealing. Cost functional involves minimization of maximum response amplitude in a set of predefined time intervals and maximization of total energy absorbed in the first loop. Dependence of the global optimum on several combinations of leading parameters of the simulated annealing procedure, like neighbourhood definition and annealing schedule, is also studied and analyzed. Obtained results facilitate the design of elastomeric cellular materials with improved behaviour in terms of dynamic stiffness for passive vibration control.

Including context in a routing algorithm for the internet of things

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia Informática

A importância de uma intranet na transmissão da missão e valores da organização: estudo de caso numa indústria química

Relatório de estágio apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Gestão Estratégica das Relações Públicas.

Reason Maintenance Systems: Tools for Foundations-based Belief Revision

Dynamic and distributed environments are hard to model since they suffer from unexpected changes, incomplete knowledge, and conflicting perspectives and, thus, call for appropriate knowledge representation and reasoning (KRR) systems. Such KRR systems must handle sets of dynamic beliefs, be sensitive to communicated and perceived changes in the environment and, consequently, may have to drop current beliefs in face of new findings or disregard any new data that conflicts with stronger convictions held by the system. Not only do they need to represent and reason with beliefs, but also they must perform belief revision to maintain the overall consistency of the knowledge base. One way of developing such systems is to use reason maintenance systems (RMS). In this paper we provide an overview of the most representative types of RMS, which are also known as truth maintenance systems (TMS), which are computational instances of the foundations-based theory of belief revision. An RMS module works together with a problem solver. The latter feeds the RMS with assumptions (core beliefs) and conclusions (derived beliefs), which are accompanied by their respective foundations. The role of the RMS module is to store the beliefs, associate with each belief (core or derived belief) the corresponding set of supporting foundations and maintain the consistency of the overall reasoning by keeping, for each represented belief, the current supporting justifications. Two major approaches are used to reason maintenance: single-and multiple-context reasoning systems. Although in the single-context systems, each belief is associated to the beliefs that directly generated it—the justification-based TMS (JTMS) or the logic-based TMS (LTMS), in the multiple context counterparts, each belief is associated with the minimal set of assumptions from which it can be inferred—the assumption-based TMS (ATMS) or the multiple belief reasoner (MBR).

Consistency and Context Management in a Multi Agent Belief Revision Testbed

Multi-agent architectures are well suited for complex inherently distributed problem solving domains. From the many challenging aspects that arise within this framework, a crucial one emerges: how to incorporate dynamic and conflicting agent beliefs? While the belief revision activity in a single agent scenario is concentrated on incorporating new information while preserving consistency, in a multi-agent system it also has to deal with possible conflicts between the agents perspectives. To provide an adequate framework, each agent, built as a combination of an assumption based belief revision system and a cooperation layer, was enriched with additional features: a distributed search control mechanism allowing dynamic context management, and a set of different distributed consistency methodologies. As a result, a Distributed Belief Revision Testbed (DiBeRT) was developed. This paper is a preliminary report presenting some of DiBeRT contributions: a concise representation of external beliefs; a simple and innovative methodology to achieve distributed context management; and a reduced inter-agent data exchange format.

Automated illustration of multimedia stories

Submitted in part fulfillment of the requirements for the degree of Master in Computer Science

Self-scheduling and bidding strategies of thermal units with stochastic emission constraints

This paper is on the self-scheduling problem for a thermal power producer taking part in a pool-based electricity market as a price-taker, having bilateral contracts and emission-constrained. An approach based on stochastic mixed-integer linear programming approach is proposed for solving the self-scheduling problem. Uncertainty regarding electricity price is considered through a set of scenarios computed by simulation and scenario-reduction. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. A requirement on emission allowances to mitigate carbon footprint is modelled by a stochastic constraint. Supply functions for different emission allowance levels are accessed in order to establish the optimal bidding strategy. A case study is presented to illustrate the usefulness and the proficiency of the proposed approach in supporting biding strategies. (C) 2014 Elsevier Ltd. All rights reserved.

Probabilistic consensus clustering using evidence accumulation

Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.

Evaluation of the Electric Vehicle Impact in the Power Demand Curve in a Smart Grid Environment

Smart grids with an intensive penetration of distributed energy resources will play an important role in future power system scenarios. The intermittent nature of renewable energy sources brings new challenges, requiring an efficient management of those sources. Additional storage resources can be beneficially used to address this problem; the massive use of electric vehicles, particularly of vehicle-to-grid (usually referred as gridable vehicles or V2G), becomes a very relevant issue. This paper addresses the impact of Electric Vehicles (EVs) in system operation costs and in power demand curve for a distribution network with large penetration of Distributed Generation (DG) units. An efficient management methodology for EVs charging and discharging is proposed, considering a multi-objective optimization problem. The main goals of the proposed methodology are: to minimize the system operation costs and to minimize the difference between the minimum and maximum system demand (leveling the power demand curve). The proposed methodology perform the day-ahead scheduling of distributed energy resources in a distribution network with high penetration of DG and a large number of electric vehicles. It is used a 32-bus distribution network in the case study section considering different scenarios of EVs penetration to analyze their impact in the network and in the other energy resources management.